Mapping connections in signaling networks with ambiguous modularity.

Abstract:

Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.

SEEK ID: https://seek.lisym.org/publications/177

PubMed ID: 31149348

Projects: LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP)

Publication type: Not specified

Journal: NPJ Syst Biol Appl

Citation: NPJ Syst Biol Appl. 2019 May 23;5:19. doi: 10.1038/s41540-019-0096-1. eCollection 2019.

Date Published: 1st Jun 2019

Registered Mode: Not specified

Authors: D. Lill, O. S. Rukhlenko, A. J. Mc Elwee, E. Kashdan, J. Timmer, B. N. Kholodenko

help Submitter
Activity

Views: 2335

Created: 24th Jul 2019 at 12:24

Last updated: 8th Mar 2024 at 07:44

help Tags
help Attributions

None

Powered by
(v.1.14.2)
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH